Objective To investigate whether correlation existsbetween quantitative perfusion parameters obtained from dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)and different prognostic factors or immunohistoch...Objective To investigate whether correlation existsbetween quantitative perfusion parameters obtained from dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)and different prognostic factors or immunohistochemical subtypes of breast cancers.Methods A retrospective analysis of DCE-MRI was performed in展开更多
Cerebral perfusion computed tomography(PCT)is an important imaging modality for evaluating cerebrovascular diseases and stroke symptoms.With widespread public concern about the potential cancer risks and health hazard...Cerebral perfusion computed tomography(PCT)is an important imaging modality for evaluating cerebrovascular diseases and stroke symptoms.With widespread public concern about the potential cancer risks and health hazards associated with cumulative radiation exposure in PCT imaging,considerable research has been conducted to reduce the radiation dose in X-ray-based brain perfusion imaging.Reducing the dose of X-rays causes severe noise and artifacts in PCT images.To solve this problem,we propose a deep learning method called NCS-Unet.The exceptional characteristics of non-subsampled contourlet transform(NSCT)and the Sobel filter are introduced into NCS-Unet.NSCT decomposes the convolved features into high-and low-frequency components.The decomposed high-frequency component retains image edges,contrast imaging traces,and noise,whereas the low-frequency component retains the main image information.The Sobel filter extracts the contours of the original image and the imaging traces caused by the contrast agent decay.The extracted information is added to NCS-Unet to improve its performance in noise reduction and artifact removal.Qualitative and quantitative analyses demonstrated that the proposed NCS-Unet can improve the quality of low-dose cone-beam CT perfusion reconstruction images and the accuracy of perfusion parameter calculations.展开更多
Objective: to study the characteristics and application of spiral CT in early lung cancer. Methods: from January 2020 to November 2021, 40 patients of suspected early lung cancer, all received 64 spiral CT scan and pa...Objective: to study the characteristics and application of spiral CT in early lung cancer. Methods: from January 2020 to November 2021, 40 patients of suspected early lung cancer, all received 64 spiral CT scan and pathological examination, referring to the diagnostic value of 64 layers of spiral CT, and the characteristics of 64 spiral CT imaging and CT of early lung cancer cases with CT perfusion parameters. Results: the pathological examination results of 40 patients suspected early lung cancer were 30 malignant, 10 benign, 64 spiral CT showed malignant, 11 benign, 64 layers of spiral CT was 97.50%, 96.67%, specificity was 100.00%;64 spiral CT confirmed malignant cases, deep segmentation, fine spur, spike, the detection rate of vascular tract collection and vacuolar signs was higher than that in benign cases. Blood flow, permeability, blood volume, and mean passage time CT perfusion parameters were higher than benign cases, and the difference was all statistically significant (P <0.05). Conclusion: the 64-layer spiral CT imaging features, fine burr features and spike features are common in the initial diagnosis of early lung cancer patients, and the blood flow, permeability and blood volume are at high levels, which can provide a practical basis for the differentiation between disease diagnosis and benign and malignant.展开更多
文摘Objective To investigate whether correlation existsbetween quantitative perfusion parameters obtained from dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)and different prognostic factors or immunohistochemical subtypes of breast cancers.Methods A retrospective analysis of DCE-MRI was performed in
基金supported in part by Science and Technology Program of Guangdong (No. 2018B030333001)the State’s Key Project of Research and Development Plan (Nos. 2017YFC0109202,2017YFA0104302 and 2017YFC0107900)the National Natural Science Foundation (Nos. 81530060 and 61871117)
文摘Cerebral perfusion computed tomography(PCT)is an important imaging modality for evaluating cerebrovascular diseases and stroke symptoms.With widespread public concern about the potential cancer risks and health hazards associated with cumulative radiation exposure in PCT imaging,considerable research has been conducted to reduce the radiation dose in X-ray-based brain perfusion imaging.Reducing the dose of X-rays causes severe noise and artifacts in PCT images.To solve this problem,we propose a deep learning method called NCS-Unet.The exceptional characteristics of non-subsampled contourlet transform(NSCT)and the Sobel filter are introduced into NCS-Unet.NSCT decomposes the convolved features into high-and low-frequency components.The decomposed high-frequency component retains image edges,contrast imaging traces,and noise,whereas the low-frequency component retains the main image information.The Sobel filter extracts the contours of the original image and the imaging traces caused by the contrast agent decay.The extracted information is added to NCS-Unet to improve its performance in noise reduction and artifact removal.Qualitative and quantitative analyses demonstrated that the proposed NCS-Unet can improve the quality of low-dose cone-beam CT perfusion reconstruction images and the accuracy of perfusion parameter calculations.
文摘Objective: to study the characteristics and application of spiral CT in early lung cancer. Methods: from January 2020 to November 2021, 40 patients of suspected early lung cancer, all received 64 spiral CT scan and pathological examination, referring to the diagnostic value of 64 layers of spiral CT, and the characteristics of 64 spiral CT imaging and CT of early lung cancer cases with CT perfusion parameters. Results: the pathological examination results of 40 patients suspected early lung cancer were 30 malignant, 10 benign, 64 spiral CT showed malignant, 11 benign, 64 layers of spiral CT was 97.50%, 96.67%, specificity was 100.00%;64 spiral CT confirmed malignant cases, deep segmentation, fine spur, spike, the detection rate of vascular tract collection and vacuolar signs was higher than that in benign cases. Blood flow, permeability, blood volume, and mean passage time CT perfusion parameters were higher than benign cases, and the difference was all statistically significant (P <0.05). Conclusion: the 64-layer spiral CT imaging features, fine burr features and spike features are common in the initial diagnosis of early lung cancer patients, and the blood flow, permeability and blood volume are at high levels, which can provide a practical basis for the differentiation between disease diagnosis and benign and malignant.
